'during') and verb ('hold') and contribute to the frequencies of corresponding features in preposition and verb vector, with respective weighting factors S p and S v , referred as the Strength parameters.

a higher value of S p and S v means greater contribution of verb+preposition paraphrases in the classification model.


We have selected only those NCs for the experiments for which atleast 3 paraphrases are found.

experiment is performed with 241 NCs (out of 326 Noun-Noun pairs) for which this criteria is satisfied.


The model employs a Best-Selection strategy which does nothing more than selecting the more suitable model for classification for any given test instance.

for the model to be efficient, it must satisfy two conditions: a) The constituent models must be complimenting.


A language model training corpus may contain many topics.

the corpus can be divided into topic-specific subcorpora.


clitics are typically segmented in a preprocessing step.


Step: The Arabic part of the bitext was systematically segmented to train the phrase tables.

each Arabic word of the training corpus is replaced by its segmentation according to the "proclitic stem enclitic" form, as described in section 3.2.


The final output of the decoder will be also composed of segmented words.

these words must be recombined into their surface forms.


Therefore these words must be recombined into their surface forms.

we apply reconstruction of the Arabic segmented words just by agglutinating the morphological segments in the following order: Proclitic + stem + enclitic.


Moreover, implicit realizations of cyberbullying are hard to recognize as they are devoid of lexical cues including profanity.

we will explore the use of more advanced features (e.g.


In common sense, syntactic structure may keep a crucial part for human being to understand the meaning of a given text.

it also may help to identify the semantic equivalence between two given texts.


Then, the expressions in square brackets are eliminated since they occur in one expression and do not occur in another.

we obtain {NN-camera, PRPwith, NN-zoom]}, which is a syntactic analog as the semantic generalization above.


In order to assess the impact of syntactic structure in the STS task, we not only examine the syntactic structure alone, but also combine it with features learned from the most common approach, bag-of-words.

we use a bag-of-word baseline to evaluate the performance of syntactic approaches.


Finally, this combination does not make any improvement over the baseline.

this combination of syntactic approaches cannot solve the STS task.


For example, people with intellectual disability have been found to have decreased working memory capacity (a characteristic they share with some people with autism), which results in their difficulty in remembering relations within and between sentences .

features developed for and evaluated on this reader population include entity density (counts of entities such as person, location and organisation per sentence) and lexical chains (synonymy or hyponymy relations between nouns) Huenerfauth et al., 2009).


The final step is to score these patterns according to their significance in indicating the occurrence of a person"s name.

each pattern in the three lists is scored using association rules support measure (Agrawal et al., 1993).


Inspired by IR models, we treat CST as a kind of structure retrieval and measure how well a ranking fits to the set of gold trees.

we adapt the R-Precision score (Buckley and Voorhees, 2000) as given in (5): where R is the number of gold trees and top-R(sys trees) refers to the R highest-ranked system trees.


Some examples are: (1) speech recognition paired with text-to-speech; (2) image captioning paired with image generation; and (3) question answering paired with question gener-ation.

a possible future direction would be to design and test the round-tripping approach for more tasks beyond MT.


Indeed, the key idea is to form a closed loop so feedback signals are extracted by comparing the original input data with the final output data.

if more than two associated tasks form a closed loop, this approach can applied in each task for improvement of the overall model, even in the face of unlabeled data.


Among the 6,060 sentences enhanced with English PropBank roles, 848 sentences did not have a predicate in Turkish proposition bank.

in 5,212 sentences, 44,779 word annotations were compared.


When applying fastText's pretrained vectors, 5 we obtained comparable results for English and French without any significant gain, and with lower performance on Bulgarian.

the final model chosen for its overall best performance across all three languages was trained without the use of an additional language model.


The underscore denotes the position for which the language model predicts possible words.

lexical substitutes are obtained independently from the forward and the backward LM and then united.


Since the corpus was extracted from novels and news sites, the sentences are assumed to include only a few or no orthographic errors.

the spellings of the "de/da" cases are considered to be correct when written separately, attached as a locative suffix, or used as a conjunction.


The alignments are progressively obtained through external resources or by inference.

it can be considered as a union of word senses lexicalized or identified in the languages covered by the MLSN.


This process creates a large amount of superflous template/lexeme pairs.

the lexicon is subsequently filtered using Expectation Maximisation.


In this paper, we introduce QBERT, a Transformerbased architecture for contextualized embeddings which makes use of a coattentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task.

we are able to train a WSD system that beats the state of the art on the concatenation of all evaluation datasets by over 3 points, also outperforming a comparable model using ELMo.


CLM is inherently unidirectional, as the model must not be able to "peek" at the word it has to predict.

to encode the left and the right contexts two separate networks have to be used, even if they often share part of the weights and are jointly trained.


We refer to the resulting sequences as, respectively, P and F .

the ith position of P encodes information from tokens w 1:i−1 while F encodes w i+1:n .


By including the same target word in each element of the pair, the dataset is constructed in such a way that contextinsensitive word embeddings would not perform better than the random baseline.

the dataset is an ideal evaluation set for assessing the quality of the semantic information encoded in contextualized embeddings.


because it is sampled from the same text).

a model can only be as consistent as the input data it is trained on and it requires the experimenter to compute data consistency in addition to vector space consistency.


Given a pre-trained background space kept frozen across experiments, the vector representation of a target is generated by simple vector addition over its context words.

the obtained vector directly represents the context the target term occurs in, and consequently, similar representations (in terms of cosine similarity) mean that the target term is used in a similar way in different parts of a book/corpus, and is thus consistently learnable.


The most important advantage of pre-training is the possibility of using unlabeled data, which is really helpful given the high costs of data annotation.

the effect of pre-training with very large datasets observed in the experiments is the most surprising result of the paper (Erhan et al., 2010): the early examples determine the basin of attraction for the remainder of the training and the supervised fine-tuning cannot escape from it.


In this situation, the number of turns is too small compared with the number of slots from the hotel domain, and the agent trained on the source domain is not able to learn.


As expected, the number of extra slots in target domain has a bigger influence over the final results.

the relative average decrease of the success rate for the agent with transfer learning is 9.5% for each extra slot, while the source domain contains only common slots.


Reliability of our model is important to ensure consistent results.

in addition to 10fold cross validation, we carry out 10 iterations of the All features model, reporting on mean precision, recall, F1, Accuracy and variance in Table  3.


Twitter is an increasingly popular social media platform among the Arabic speaking people who tend to frequently use their Arabic dialects when sharing their stories and opinions.

we focused on Twitter to collect the data for our corpus.


For example, the adjectives that describe feminine in Arabic usually end with (Taa' Marbootah).

finding words that end with the Arabic letter Taa' Marbootah such as (Feeling cold), (Feeling sleepy), or (Feeling hungry) in the tweets of a user indicates that the writer is a female.


Given its linguistic motivation, the Big Five personality traits have been addressed in a wide range of studies in both natural language understanding and generation alike.

for instance, the work in Mairesse and Walker (2007) introduces PERSON-AGE, a fully-functional NLG system that produces restaurant recommendations.


In UDA, the adaptation task does not vary between the source and target domains.

we can use S L to learn a projection for the source domain S prj where the separation between an instance x ∈ S L and its opposite-labelled nearest neighbours is maximised.


However, in our preliminary experiments, we found that this process does not improve the performance in UDA beyond the first iteration.

we limit the number of iterations to one as shown in Algorithm 1.


In order to robustly evaluate the performance of an UDA method we must evaluate on multiple domains.

in addition to MAD, we also evaluate on an extended dataset that contains 16 domains.


As discussed in Section 3.2, similar to Self-Adapt, we observed that the performance did not significantly increase beyond the first iteration for any of the classical self-training methods in UDA.

we compare all classical self-training methods for their peak performance, obtained after the first iteration.


Recent NMT models treat a sentence as a sub-  word sequence rather than a word sequence to address the translation of out-of-vocabulary words (Sennrich et al., 2016).

we extend dependency-based self-attention to operate at subword units created by byte pair encoding (BPE) rather than word-units.


Self-Attention Recent NMT models have improved the translation performance by treating a sentence as a subword sequence rather than a word sequence.

we extend dependency-based selfattention to work for subword sequences.


Yet, there are limitations in how much it is possible to handle manually in a short period of time (and time is very critical as toxic content spreads fast).

an attractive alternative is to use machine learning and natural language processing to automate the process of toxic news detection.


Annotation was performed on entity level.

we did not have to care about token boundaries in the surrogate generation step and, thus, no special handling for compounds and multi-token entities is required.


(2015b) we did not implement any other restrictions for the selection of characters.

the resulting surrogates may have an unrealistic appearance.


For instance, an Automated Readability Index (ARI) has the following form for the document d: where c refers to the total number of letters in the document d, w is the total number of words and s denotes the total number of sentences in d. Since readability indexes rely on a few basic factors, precise assessment requires aggregation of many scores.

coh-Metrix-PORT tool (Aluisio et al., 2010) includes more than 50 different indexes for Portuguese language.


The second model presented in this paper is based on the assumption that each term has an independent fixed complexity in the whole language.

in this section we consider not the complexity distribution of a single term, but the general complexity distribution over all terms in the language.


The described distribution for the Russian Wikipedia reference collection is shown on Figure 3.

we have defined c(t) for all terms possible and the distribution necessary to count the C γ .


This evaluation indicates that the automatic sentence simplification method facilitates IE.

of various difficulties identified in current approaches to intrinsic evaluation of sentence simplification methods, we performed an extrinsic evaluation of one information-preserving sentence simplification method via three NLP tasks.


13 We found that the simplification step had no impact on this task.

although the findings reported in our current paper seem promising, it is difficult to know the extent to which they are applicable to other NLP tasks or to tasks which differ only with respect to the test data used.


This aspiration, however, was probably not phonemic in Old Tibetan (Hill, 2007, 471).

a set of string replacement rules in the Cg3 formalism was created to normalise and convert these instances to their equivalent reading in Classical Tibetan.


Fourteen approaches were tested over a real dataset containing 10K rulings from the Brazilian Supreme Court (Feijó and Moreira, 2018).

another contribution is using a language that is not typically included in summarization experiments.


In the legal domain, it is common to reference existing laws, specific dates, and names.

it is very unlikely that the vocabulary generated during training will contain all possible words that are present in the test set.


It assumes that the main idea of a text is often paraphrased.

finding similar sentences would be the same as finding the important sentences.


Missing or referring to an incorrect precedent may cause the petition to be denied and the case would be lost.

considering the results seen so far, neither approach delivers results that could safely Algorithm R1-F R1-P R1-R R2-F R2-P R2-R RL-F RL-P RL-R   Table 4: ROUGE scores using abstractive models.


In this regard, Skory and Eskenazi (2010) observe that Shannon's information theory (Shannon, 1948) could be used to estimate the reading difficulty of answers to a gap based on their probability of occurrence.

for the sentence "She drives a nice ", the word "car" would be the most likely answer (lowest readability level) while words such as "taxi", "tank" and "ambulance" would be at increasingly higher levels.


Existing abstractive summarizers often rely on an extractive pre-processing component to produce the abstract of the text (Berg-Kirkpatrick et al., 2011;Knight and Marcu, 2000).

in this paper we focus on extractive summarization methods, also given the fact that lyrics i) strongly use figurative language which makes abstractive summarization even more challenging; and ii) the choice of the words by the composer may also have an importance for capturing the style of the song.


it is worthwhile to see if methods in audio summarization can be transferred to lyrics summarization.


Results When evaluating each of the 12 genres, we found two clusters of genres to behave very similarly.

we report the results for these two groups: the Rap genre cluster contains Hip Hop, Southern Hip Hop, and Gangsta Rap.


For the lexicon of produced forms (output lexicon), quality check mainly involves examining tokens with unusual structure that deviates from the phonology of the ambient language.

in the proposed approach, one estimates the potential of lexical entries to contain errors, and then focuses on suspicious forms.


A single letter can host both consonant and vowel diacritics (the C_diac and V_diac groups in Table 2, respectively).

the string ָ ,בּ pronounced /ba/, is composed of the letter ב (representing the consonants /b/ and /v/), a consonant diacritic ּ◌ (specifying the consonant /b/), and a vowel diacritic ָ◌ (representing the vowel /a/).


The system is incorporated in a general-purpose linguistic software that includes tools for building dictionaries, as well as corpus analysis functions.

the described system can help studying the phonological properties of text corpora and it is also useful for creating resources for under-resourced languages.


We can also note that SES is a complex unit of measurement of a person's economic and sociological standing, like for instance, his prestige, power or else his economic well-being (Hoff et al., 2002;Oakes and Rossi, 2003).

one can conclude that the SES is a complex measure of evaluation that differs from a research to another because it takes into account the work experience, the economic position, or the social status.


through relative clauses or adjectives, can be optionally removed.

they use recall and two kinds of precision in the evaluation in order to account for the feature of minimality.


Tweet mostly include many abbreviations, typos, or language errors that result in relatively high variability of the language and complexity of the statistical picture.

we need to transform the original texts into a representation reducing this complexity.


Nonetheless, the very fact of using such extra-linguistic tweet elements, their placement and frequency might be important and relevant to authors' tweeting styles.

we replaced all occurrences of extralinguistic elements with symbols representing their types: • hashtags were exchanged with '#' sign, • mentions with the '@' sign, • URLs with the 'ň' sign (not present in neither Tweet Corpus nor Influencer Set).


This data set does not contain external data for language model training, so our approach of using a separate corpus for this task could not be applied.

in this experiment our solution differs only in the method for text encoding -the baseline  Table 2: Accuracy results for (Schwartz et al., 2013) data set, compared with results obtained in (Shrestha et al., 2017) for 50 authors.


embedding vectors are built for BPE codes, not words).

the proposed methods seems to be better suited for processing short texts in a highly inflectional language.


Unlike previous work, our neural approach is independent of the performance of the IR system, as it does not build upon it.

it is possible to achieve improvements over both systems by combining them, as each system has an individual contributions towards solving the problem.


(A) Erythrocyte casts (B) Glucose (C) Leukocyte casts (D) Oval fat bodies (E)* Uric acid crystals  latter is in order to keep the random guess baseline at a constant for all items (20%).

the final data that we have is 164 items for the public set and 922 for the private one.


That is why LM and NMT are usually trained with the sum of cross-entropies over the target sentence.

although language related tasks are more of generation rather than classification, the models estimate target probabilities with the softmax operation on the previous neural network layers and the target distributions are provided as one-hot representations.


When two words have semantically or syntactically similar meanings, the words are represented closely to each other in a word embedding space.

even when the prediction is not exactly correct, the predicted word might not be so bad, if the estimated word is very close to the target word in the embedding space like 'programming' and 'coding'.


Basically, knowledge distillation provides more information to new models for training and improves the new model's performance.

when a new model which is usually smaller is trained with the distilled knowledge from the trained deep model, it can achieve a similar (or sometimes even better) performance compared to the pretrained deep model.


Words in the embedding space have semantic and syntactic similarities, such that two similar words are close in the space.

when the classification is not correct, the error can be interpreted differently depending on the similarity between the predicted word and the target word.


That is, we cannot extract any knowledge from the training model at the beginning.

we start training process without knowledge distillation at first and gradually increase the amount of knowledge distillation as the training iteration goes.


We found that the kappa coefficient for this ranges from 0.97 to 1.0.

our manual term translation classification quality can be labeled as excellent.


The latter can be very general, or can contain insignificant rare words, which can bias the search.

instead of using only the first-hit document, we should also evaluate lower-ranked ones.


The second paragraph manages to extract the definition of relativism and to give good supporting evidence for the correct answer, namely that there is no absolute good and evil (B).

this option is assigned high probability.


Therefore, a random sample of social media messages can be written in several languages.

systems that detect offensive posts without relying too much on language dependent features would be valuable in real life scenarios.


If we used word embeddings to initialize the embedding matrix in the embedding layer, most of the words that are fed will be out-of-vocabulary words.

we used character embeddings (Mikolov et al., 2018) as it provides embeddings for misspelling words and new words.


This drawback makes the training of the LSTM-attention model inefficient.

the trained LSTM-attention model predicts the same label for all inputs.


We observe that a set of salient words in each domain is different.

the support of attention signals in domain adaptation is not significant compared to the recurrent models without attention.


However, the ROUGE metric measures the number of matches between two pieces of text, so it is difficult to compare which one is more informative.

we execute an informative test to understand whether aspect signals help to generate a more informative summary.


In the corpus, we observe that sentences carrying the most polarity are not suited to summarization.

employing sentiment for summarization needs deeper analysis.


Several Integer Linear Programming (ILP)based summarizers (Hirao et al., 2013;Kikuchi et al., 2014) use the discourse information given by a discourse parser (Hernault et al., 2010).

the performance of the summarizers is strongly affected by the performance of the discourse parsers.


In addition, recursive attention modules in our model can consider multi-hop dependencies between sentences.

our model can capture the relationships between sentences effectively and create a summary without losing the coherence between sentences.


The dataset is anonymized; all named entities were replaced by the special token @entity, and paragraph boundaries were deleted.

we used the non-anonymized version of the dataset provided by Hermann et al.


These differences are statistically significant with the sign test (p < 0.05).

human evaluation also supports the effectiveness of incorporating discourse information.


We call this value τ v,t and define it as fol-lows: (3) δ x,y denotes the Kronecker delta.

we take the expectation over all possible graphs for the given vocabulary, and then over all possible taggings for a fixed graph.


Semi-automatic approaches on the other hand often result in lower quality data blurred with statistical noise.

the best performing supervised approaches to WSD task are usually limited to a specific narrow subset of training vocabulary.


all synsets of this word have initial scores in v set to zero.

ppR_W2W cannot be run once for all ambiguous words in the context.


The words read, paper, today, drove, car and yesterday are replaced by the word CW based on the pre-defined condition.

masking some words, would create a pattern and separate the texts of some users from other users.


• User profile name features: User and screen names are very much subject to a user's choice.

we hypothesize that it provides valuable information that helps to distinguish bots from humans.


Our data set contains the manually tagged data of the 8385 accounts.

1677 records will be used for validation and 6708 data records remain for training.


academic) there were 11 annotations: 3 neutral, 4 generally negative, 3 generally positive and 1 entirely positive.

arbitrary values for word "akademicki" are: • entirely positive = 0.0909 • generally positive = 0.2727 • neutral = 0.2727 • generally negative = 0.3636 • entirely negative = 0.0000 • ambivalent = 0.0000 Using the described dictionary we have proposed additional variant of BiLSTM classifier with a word embedding vector extended with the values of sentiment for the lemma of the word from a prepared sentiment dictionary.


While the vector representations of BERT are unsupervised, they are fine-tuned on the dataset.

we put them in a separate category (System #11).


At the same time, we want to have a fair validation and investigate numerous scenarios where our approach performs well.

in this experiment, we use monolingual data to generate synthetic sentences using both NMT and USMT separately and then, each of them is used to augment the training data for the Neural Machine Translation model.


Especially for low resource languages, this constitutes a serious issue.

most NLP research on cognates has mainly focused on the automatic detection of such cognate pairs.


A disadvantage of using text-formatted pretrained embeddings is that we could not generate embeddings for all words in the gold standard list.

we only obtained word embeddings for 12,433 instances, while we have orthographic information for 14,618 instances.


However, for many predicates there are just few examples, and some semantic roles are also rare.

we followed (Shelmanov and Devyatkin, 2017) and filtered the dataset keeping only predicates that have at least 10 examples.


In the second setup, we evaluate the performance of the argument classification models for "unknown" predicates.

we divided the dataset in two parts by leaving 80% of predicates with their examples for training and 20% of predicates for testing.


The first one is Using (Is Used by ↔ Uses), a frame-to-frame relation defined as a relationship between two frames where the first one makes reference in a very general kind of way to the structure of a more abstract, schematic frame (Ruppenhofer et al., 2016, p. 83); it may be viewed as a kind of weak inheritance (Petruck, 2015) where only some of the FEs in the parent frame have a corresponding entity in the child frame, and if such exist, they are more specific (Petruck and de Melo, 2012).

the frame Arranging Uses the frame Placing and the two frames share the FEs Agent and Theme, while the more specific FE Configuration in the frame Arranging corresponds to the more general FE Goal in the frame Placing; the first frame is exemplified by the synset {arrange:1, set up:5} which is a hyponym of {put:1, set:1, place:1, pose:5} whose assigned frame is the more general frame Placing.


Recall the example To determine whether a goldfish is a pet, we calculate: pet − goldfish = pug + tabby Now, since pug and tabby are both positive, and positivity is preserved under addition, we know that pet − goldfish is also positive.

under either of our graded measures, the extent to which a goldfish is a pet is 1.


This is not surprising, given that semantic information, e.g., hypernyms, and classes from an ontology, typically impose strong constraints on the types of the entities participating in a relation, indicating that such kind of feature can have significant discriminative power in RE.


People are using social networks more and more to express emotions, sentiments and mental states.

the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders.


However, this study only focuses on English and it is important to prove that the systems can also be applied to other languages.

in this paper we create a Spanish corpus from Twitter with information concerning of anorexia and non-anorexia data.


It shows that there were more false positives (300) than false negatives (178).

we analyzed some of these tweets manually in order to find the main reasons why our system can be confused.


In many other cases, however, a lump type may be preferred.

nLC features types for morphemes, function types, lump types and polymorphism.


syntax and morphology emerge as by-products of a contingent conformation to the serial channel over which language is processed.


During the simulations, we instantiate two bots; the system and the user.

for each conversation, we pick up a user profile, then user and system bots carry on the conversation through pseudo-language actions regarding  every conversation is represented as an exchange of actions between the two agents.


The Single Memory Network does so by incorporating all the intents in its selection of the responses.

it searches for more general responses while the Multiple Memory Network assigns the same task to a specialized Memory network, which is trained on that very specific intent.


One possible explanation is that the tests have not been conducted in an OOV setting.

the SMN and MMN may not learn the linguistic patterns to find entities (templates) but they directly learn to recognize the entities and to predict the API calls accordingly.


giving an intent together with unexpected slots, see example in Table 4).

the system has no context (no interaction memory) to reason on while selecting the response.


Analyzing such huge volumes of data manually could lead to the waste of time and investment.

currently more automated and efficient ways are implemented to solve the problem.


Firstly, not all words have sentiment value despite how frequently it is used in the document, namely, frequency of commonly used words could shadow other more significant and sentiment containing words.

to solve this problem term-frequency and inverse-document-frequency method (tf-idf) is widely used.


Having data skewness can have a direct impact on the results of a machine learning model and gaining further insights can contribute to obtaining higher results.

the impact of skewness on our dataset had been researched.


Table 4 demonstrates that skewness in the dataset affects the performance of the classifier.

we explored several approaches for optimizing the results while working with skewed dataset.


In fact, no learning corpus can make it possible to observe all the sequences of possible words.

the basic idea of n-gram models consists in considering only the sequences of words of length n, i. e the calculation is approached by a limited history consisting of the n-1 preceding words.


It is fundamental to take into account the contextual information in order to better distribute the words into the classes.

the use of contextual information is of interest in the context of improving the predictability of the model.


This approach is based on a pivot language (called IF for Interchange Format), which represents the meaning of the sentence regardless of the language (2).

the criterion of choice of classes is guided by the definition of the pivot language and the most used concepts in the IF.


Only the grouping of these words with other words can give a better semantic value to words that may be insignificant and subsequently useless for our field of work.

we have decided to create semantic blocks that consist of grouping one or more words into a single word.


In this task, each word or semantic block is labeled with a concept indicating its appropriate semantic nature.

semantic labeling is not done word by word because we can find words that can have several meanings depending on the context in which they are used.


In fact, a semantic class may correspond to a label or a group of labels, whereas a label cannot belong to only one class.

if we consider the two statements: Sentence 1: [Tunis second class].


As we have already mentioned, a low value of perplexity reflects a strong predictive power of LM.

to better judge our choice of creating an n-class word model, we compared the created model with the 3-gram type model on the same test corpus of evaluation.


This can be more blatant when one deals with under-resourced languages, such as the Tunisian dialect, which suffers from unavailability of basic tools and linguistic resources, like sufficient amount of corpora, multilingual dictionaries, morphological and syntactic analyzers.

this language processing faces greater challenges due to the lack of these resources.


The manifestation of this interaction between these languages is obvious in introducing borrowed words.

the lexical register of the Tunisian dialect seems to be more open and contains a very rich vocabulary.


The disadvantage of this approach is that all non-standard words (NSWs) have to be represented in the dictionary in order to obtain the corresponding normalization.

the success of this kind of systems highly depends on the dictionary coverage.


Our survey has 18 questions, of which many were conditional and show only if they apply to the respondent.

not all questions have been answered by all participants, while most multiplechoice questions allow for several answers, resulting in more answers than participants for these questions.


Another broadly used method it to learn an embedding matrix for each time slice independently; due to the stochastic aspect of word embeddings, the vectorial space for each time slice is different, making them not directly comparable.

authors perform an alignment of the embeddings spaces by optimising a geometric transformation (Hamilton et al., 2016;Dubossarsky et al., 2017;Szymanski, 2017;Kulkarni et al., 2015)).


These embeddings are representative of the use of words in 2013; and in general, large corpora exist almost exclusively for recent periods.

we choose to use the pre-trained embeddings as initialisation for the last time step (the most recent).


However, both have low ability to discriminate the highest drifts on a very small dataset.

we added a regularisation term to their loss using the Hardshrink activation function, successfully getting longer distribution tails for the drifts.


Morphological analysis tends to occupy a larger part of the processing time in the tasks.

we investigated a new morphological analysis method that improves efficiency and performance.


The lists, each containing 120 words, were manually adjusted by removing noncognates by possibly substituting them with etymologically related items, if such could be found, and adding further cognates 3 .

for exam-ple, BG-RU ние-мы (nie-my) 'we' was removed and the BG в р (zvjar) 'beast' instead of ивотно (životno) 'animal' was added to its RU cognate вер (zver') 'animal, beast'.


However, identical orthographic correspondences may still have a small surprisal value, for example, from a RU perspective the correspondence a:a has a surprisal value of 0.5986 bits resulting in an increase of the WAS value.

we decided to manually modify our WAS calculation in such a way that all identical orthographic correspondences are measured with 0 bits.


It must be mentioned here that the RU stimuli are in general longer (5.09 characters) than the BG stimuli (4.61 characters).

the BG readers should intuitively delete more characters while the RU readers should add more characters in order to guess the correct cognate.


This means that the slightly higher entropy is found in the language pair where there is slightly lower intelligibility.

we were able to show that conditional entropy can be a reliable measure when explaining small asymmetries in intelligibility.


Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL.

lD2Nl is capable of generating either a single sentence or a summary of a given resource, rule, or query.


Also, SPARQL2NL  uses rules to verbalize atomic constructs and combine their verbalization into sentences.

our goal with LD2NL is to provide a complete framework to verbalize SW concepts rather than become the state of the art on the respective tasks.


Yet, the aggregation can still be improved: if there is any atomic class in the conjunction, we know that this is more specific than the placeholder everything.

we can replace it by the plural form of the class, finally resulting in cities that are located in France.


In overall, our approach is effective at reducing redundant information in abstractive summaries, how-  ever this comes with a potential loss of information, which can lead to a reduction in ROUGE.

our denoising methods are currently better suited for extractive than for absctractive summarization.


When creating validation sets special care is taken to remove duplicates and to include data from all individual clients and all classes in a randomized manner.

largely balanced validation sets are formed with data from various sources.


Quotations are a prominent linguistic device used to express claims, assessments, or attitudes attributed to speakers.

the analysis of quotations is gaining traction in computational linguistics and digital humanities, providing evidence for speaker relationships Agarwal et al., 2012), inter-speaker sentiment (Nalisnick and Baird, 2013), politeness (Faruqui and Pado, 2012), and narrative structure (Jannidis et al., 2018).


Note that these results, as well as the earlier studies (Pareti et al., 2013;Scheible et al., 2016), use unrealistic gold standard features.

we ran a second version of NQD using only word features, but no tags or structural information.


A relation block is a similar computational graph, but with an input variable whose size is twice the entity output size.

the relation input shape corresponds with the output shape of the two entity blocks that will be connected.


We wanted to test the effectiveness of our cleaning methods.

we tested our methods with both types of text: texts where only the line breaks have been removed and texts that have been completely cleaned.


Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.

techniques for augmenting the training set have become popular recently.


However, this approach suffers from several fundamental limitations (Alekseev, 2018).


Third, we treat each cluster as a separate collection and perform K-Means algorithm again.

we obtain both first-level and second-level clusters.


(2015): using the UKB system's random walk function to produce an artificial corpus of node sequences, then training a Skip-gram model (Mikolov et al., 2013) over it.

for each relation in the KB of the kind (synset_1, synset_2) its weight is set to the value of the cosine similarity function between the two synsets.


The dependency analyses of the sentences are not part of the original annotation of SemCor.

those cannot be reused for the construction of sense-based relations.


Each word sense in WordNet is connected to a lemma and a synset.

we could use either the synset embeddings, or the lemma (word) embeddings, or some combination between them.


Adequacy is even more problematic for ambiguous words which have two or more meanings depending on the context.

ambiguity of nouns, verbs and pronouns has been investigated extensively in recent years (Guillou et al., 2018;Müller et al., 2018;Rios Gonzales et al., 2017.


It should be noted, though, that the conjunction ambiguity is more structural than lexical: it is mainly related to certain aspects of grammar involving the arrangement of words and word types.

the conjunction ambiguity is related more to fluency than it is to adequacy.


Our only information for SRL comes from UD trees.

the resulting model can be applied to any of the supported languages.


In our demonstration application, we show a multi-language model that is capable of producing annotations for many languages.

we omit all bilingual features including the lexical features from our model.


However, there are no such training data that would contain SRL annotations on UD trees.

we proposed an algorithm to convert existing SRL annotations built on SD 1 trees.


We want to stay with the idea of one model for many languages.

we need to use crosslingual embeddings as lexical features.


This was caused by the low number of removed words (573 words out of 367,435 in total) for Polish.

the assumption that the reasoning contains only give-away words is false; leading us to believe that some information about the validity of the claim is contained in the reasoning.


In addition, calculating these features is usually not an easy task, especially for languages other than English.

in contrast to traditional ML methods, models based on neural networks can be easily applied to other languages.


However, the biggest challenge that the neural based architectures face when applied to STS tasks is the small size of datasets available to train them.

in many cases the networks can-not be trained properly.


In this way the word 'bank' in above sentences have two different embeddings.

contextualised word embeddings perform better than standard word embeddings in many natural language processing tasks like question answering, textual entailment etc.


Languages and Domains Our approach has the advantage that it does not rely on language dependent features and it does not need a training set as the approach is unsupervised.

the approach is easily portable to other languages and domains given the availability of ELMo and BERT models in that particular language or domain.


These supervised approaches always suffer from less training data problem which is common in STS tasks.

the researches have also considered unsupervised approaches.


Our approach was also applied in the Spanish STS and Bio-medical STS tasks, where our simple unsupervised approach finished on the top half of the final result list in both cases.

given our results we can safely assume that regardless of the language or the domain cosine similarity using Smooth Inverse Frequency with stacked embeddings of ELMo and BERT will provide a simple but strong unsupervised method for STS tasks.


length and frequency, POS patterns and degree of specialisation.

the supervised methodologies that have been developed are extremely difficult to compare (both to each other and to non-ML systems) and qualitative analyses that go beyond calculating precision (how many of the extracted cts are true terms), recall (how many of the true terms are extracted) and f1-scores (weighted average of precision and recall) are rare.


Finally, results are rarely discussed beyond reporting the scores, which may result in a distorted image, given the ambiguous nature of terms, as will be discussed further on.

while researchers regularly mention the suspected impact of methodology, term definitions, language and domain, little is known about how these factors influence the actual results.


Even though the gold standard was rigorously annotated, there is always the possibility of human error and the ambiguous nature of terms, which means that these annotations are not the only possible correct annotations.

it is worth looking at the ATE results in more detail.


For example, in the sentence "Зрители смогут увидеть показательные выступления спортсменов -чемпионов России и Европы" (Spectators will be able to see demonstrations of athletes -champions of Russia and Europe), prior negative relations between Russia and Europe are not mentioned.

we need an additional factor to provide the quality of the annotation, and the RuSentiFrames lexicon can be used as such a factor.


A typical RNN-LM propagates a context vector that integrates information about previous inputs to use for the next prediction.

the information that is captured at the beginning of a sequence containing a long-distance dependency is likely to have faded from the context by the time the model spans that dependency.


In that view, the contribution of an input to a given timestep can be understood as an importance score weighted by the LSTM's gating mechanism.

if something is important to the current context if should receive a larger importance score and be held in the memory block for a number timesteps.


Once again, we tie the weight matrix used for the transformation in the softmax layer to be the embedding matrix.

the dimensionality of the embeddings is set to 1,000.


Twitter messages were manually labelled by assigning a positive/negative class to all messages that unequivocally expressed a stance on the intended topic, and by assigning the class 'other' to any message that did not meet these criteria.

the class 'other' represents the fact that, despite containing a key word of interest, the message did not convey any obvious stance about the target topic, and it was therefore regarded as noise 1 .


As expected, the k-best n-grams in the models that use feature selection largely correspond to single words (e.g., 'unacceptable') or short expressions (e.g., 'I agree'), both of which clearly denoting stances in our domains.

the model is comparable to a variable-length word n-grams, but with greater flexibility to include subwords (e.g., 'believ*').


Evaluation shows that about 75% of the obtained expressions are correct, actual errors are rare.

this paper is 1. a proof of concept concerning the corpus lattice model, opening the way to investigate this structure further through our implementation; and 2. a proof of concept of the "jump and stay" idea and the algorithm itself, opening the way to apply it further, e.g.


Users can then reply to the initial post or to previously posted replies in a recursive manner.

a thread can comprise several comments structured in a tree with the submission as a main or top-level (level 0) post, which corresponds to the root of the tree.


Leaf nodes are posts which have no further direct replies.

there is always exactly one directed path from the root to any comment in a thread.


Additionally, we included the the top-level comment for each linear conversation to give information about the general topic in each case.

we define a linear conversation from our corpus as l = {c 0 , c 1 , c 2 , ..., c n }, where 2 ) > 0.5 with n = 2 * k + 2, where k ∈ N corresponds to the number of comments of the context to the left and right of the offensive comment (or window size) and there has to be a path from c 0 to c 1 .


This yields a semantic relatedness score in [0, 1] for any wordemoji pair.

we can view the score as reflecting to what extent a word correlates with an emoji.


An example is U+1F63D, which has an occurrence frequency of 88 only, compared to emojis such as "Face with Tears of Joy" U+1F602, which occurred 14,622 times.

in some cases, their results may not be reliable.


While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information.

we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity.


In order to represent the category of the NE, each tag for begin and inside tokens includes a modifier for the category.

we use nine labels: The example in Table 1 shows a simple sentence annotated with two named entities: a person name (Hristo Stoichkov) and a location name (Sofia).


While we can still build a stable system based on this data, the class imbalance makes our model more vulnerable to overfitting.

we use early stopping in order to prevent the model from continuing to learn weights and parameters if it does not see an improvement in the final score.


The key takeout of our model is that we use some feature modeling to show that for a morphologically rich language such as Bulgarian using POS and grammatical information can improve the results.

we mix automatically learned features -the word and the character embeddings -, with hand-crafted features encoded as a grammatical vector.


The most up-to-date descriptions of vulnerabilities are usually posted on specific websites and forums in form of unstructured informal texts.

a system extracting relevant cybersecurity information from unstructured publications could be of great use for a cybersecurity expert.


Since we are concerned with the identification of cognates across languages, we only use as our training data those word pairs that are either cognates, or where one word is the root of the other.

we exclude derivationally and compositionally linked word pairs from our training set.


A difficulty in evaluating on the Sami datasets is that the set of word pairs annotated as cognates in theÁlgu database is known to be far from complete for the vocabularies covered.

there are many word pairs in the vocabularies that are cognates, but are evaluated as unrelated.


Computing scores for all pairs of words between two vocabularies is time consuming.

when evaluating on whole vocabularies, we only take those words q in vocabulary X that we know have at least one cognate in the other vocabulary Y .


The motivation stems from the observation that linguistic structures tend to have different distributions in original and translated texts.

assessing the frequencies of syntactic structures in two parallel texts, especially for text chunks that are aligned with each other across two parallel sentences, may shed light on the direction of the translation.


Table 8: Results for German using sampling (69 541 features).

we again come to the conclusion that we cannot generalize across languages with regard to which feature types are useful nor to the amount of features that are useful.


Their collaborative construction importantly enables them to avoid the scaling problems encountered by expert-constructed knowledge bases.

ccKBs have come to play an important role in information systems, forming the basis for a wide range of natural language processing applications (Hovy et al., 2013) such as question answering (Berant et al., 2013;Krishnamurthy and Mitchell, 2015) or representation learning for entities (Toutanova et al., 2015;Yaghoobzadeh et al., 2018).


These values are averaged over all datapoints in the test set T s. Intuitively, MRR describes how close, on average, the predictions are to the correct one in terms of ranks: an MRR of 0.5 means that they are the second-nearest neighbors, an MRR of 0.3 means that they are the third-nearest neighbors, and so on.

higher MRR values indicate better performance.


These numbers correlate with the numbers of entities present   for these domains (Table 2): citytown is the largest domain, with almost 1800 entities in the training set, while people is the smallest domain with 85 training entities.

we surmise that the differences in performance are not due to inherent differences in the features to be predicted, but reflect the different amounts of training data available.


Furthermore, it has been recently demonstrated that semantic shift is a gradual process and not a sudden and distinctive phenomenon (Rosenfeld and Erk, 2018).

here we treat our task as a word ranking problem, where our aim is to rank the words based on their semantic shift.


An intuitive way to accommodate this is to use words that are static throughout a longer period of time.

to detect "diachronic anchor words", we first perform all of the pairwise alignments and calculate the cosine distances of the words as before.


As opposed to early work studying the change in word frequency over time to detect semantic change (Michel et al., 2011), here we are interested in detecting words that have undergone semantic change based purely on their context.

to avoid any bias towards words that have appeared at a certain point in time (e.g., "facebook"), we focus strictly on the words that appear every year, yielding a vocabulary of 168,362 unique words.


Furthermore, in our exploratory analysis, we have qualitatively demonstrated that semantic change is a gradual process.

incorporating the temporal dimension of the task in our approach is a major direction for future work.


We start with the baseline transformation to initialize the source and target embedding spaces and then our model learns an encoder based on morphological segments in the source side and their counterparts in the target space.

the transition matrix which is computed to produce a shared cross-lingual embedding space, is learned through morpheme representations and their composition in the source language.


Yet, this proved to be a very demanding manual effort, and only a relatively small number of claims could be fact-checked.

it is important to prioritize what to check.


(2010b) looked for new claims on the Web that entail the ones that have already been collected.

the task can be reduced to recognizing textual entailment (Dagan et al., 2009).


They argued that the top terms in claim vs. nonclaim sentences are highly overlapping in content, which is a problem for bag-of-words approaches.

they used a Convolutional Neural Network, where each word is represented by its embedding and each named entity is replaced by its tag, e.g., person, organization, location.


We approach the task of check-worthiness prediction as a multi-source learning problem, using different sources of annotation over the same training dataset.

we can learn to mimic the selection strategy of each of the individual sources.


Recall that our main objective is to prioritize the claims that should be selected for manual factchecking, which is best achieved by proposing a ranked list of claims.

we have a ranking task, for which we use suitable information retrieval evaluation measures.


In addition, we need to capture information that is relevant to the match, but is expressed in a semi-explicit or implicit way, such as health conditions, confidence, psyche, etc.

we formulate the following research hypothesis: We can predict the outcome of a sport match by analyzing a given pre-match interview using modern NLP and ML methods.


These two feature categories help to create datasets with filtered features.

there are 3 versions for each dataset: "All features", "Top 1 & Top 2 features" and "Top 1 features".


However, the benchmark is only for English and there is no publicly available large benchmark for low resource languages like Vietnamese (Vu et al., 2014).

we propose a new evaluation metric for the word analogy task in Section 3.


Regarding this evaluator, it is worth to note that with a huge number of possible linguistic relations (and different objectives, e.g., modeling syntactic vs. semantic properties), no embedding model is able to hold all related words close in the vector space.

only one testing schema (i.e., word analogy test) is not enough to evaluate multiple pre-trained embeddings.


To know which set of word embeddings potentially works better for a target downstream task, we limit the vocabulary of the embeddings similar to vocabulary of the task (i.e., the NER task).

only 3,135 word analogy records are being evaluated for the NER dataset (Section 4.2).


Moreover, much fewer works are devoted to tagging CMC texts than text of the standard language, while the problem of processing CMC texts is continuously growing in importance and numbers of applications.

the fast growth of social media requires appropriate adaptation, or even expansion, of the tagging methods, to serve growing interest and requirements in the processing of texts of such kinds.


Using a bidirectional LSTM layers does bring information about the context, but only before the word (or after the word when looking from the other direction) and the method of combining the results of these two directions does not guarantee focusing on both sides of context in the same degree.

our solution proposes to add to the network information about the context from the Brown clustering algorithm.


Many of the methods proposed in literature are based on a process: normalisation followed by tagger application.

a comparison of our solution with them is difficult, as the text tagged is different.


Thus, a comparison of our solution with them is difficult, as the text tagged is different.

we do not handle segmentation problems in our tagging system.


The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus.

the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution.


The analysis only considers NIL entities that were marked as such (e.g., NIL or similar designation).

the OKE 2016 counts shows no NIL entities, since they were not included in the original dataset.


In addition, web users with autism have been consistently shown to have different information searching strategies when processing web pages (Eraslan et al., 2017;Yaneva et al., 2018;Eraslan et al., 2019;, which relate to differences in visual attention.

of these difficulties, information contained in online user feedback can be less accessible for people with autism.


Constrained by these limitations, some approaches propose to automatically evaluate text simplification systems for people with autism in terms the change in readability of the generated sentences Stajner and Saggion, 2013), the incorporation of user-evaluated data into larger corpora , or the use of corpora containing texts for children and language-learners (Štajner et al., 2014).

very little is known about the perceptions of adults with high-functioning autism on the usefulness of specific simplification strategies.


Typically, Turkish words do not contain two successive vowels, except for some loanwords.

if a vowel-initial suffix is attached to a vowel-final word, a consonant (typically 'y') emerges to avoid a VV sequence, as demonstrated in (Table 3( A further phonological operation in Turkish is Vowel Harmony, which requires any vowel to agree in backness and any high vowel to agree in rounding with the preceding vowel.


The rule-based systems use predefined rules to match new utterances to their intents, and these rules need to be carefully engineered by human experts.

the advancement of these systems requires a huge amount of human effort.


In the Naive Bayes model, the frequency of a word is prominent for scoring the importance of the word for each class.

finding the correct lemmas of the words is a useful transformation to classify tweets correctly.


(2014) showed the positive impact of morphological analysis for the sentiment analysis of Turkish tweets.

in order to increase the success of the Naive Bayes and SVM models, we use a lemmatization model (Sak et al., 2008) which is trained by nearly one million Turkish sentences.


Translating this into a matrix gives the following tensor for P: To compute the truth value of 'John is a mathematician', we perform predicate-argument application as tensor contraction (matrix-vector multiplication, in this case): Likewise for 'Tom is a mathematician': Model theory for predicate calculus represents any n-ary relation R, such as a verb, as the set M R of n-tuples of elements from D for which R holds.

such relations can be viewed as functions f R : D n → B where: We can represent the boolean function for such a relation R as a tensor As was the case for predicates, the weights for relational tensors are defined in terms of the set modelling the relation: c T R 1α 1 ...α n is 1 if the tuple (x, .


So in STS, we want the similarity to be a function of the number of elements in the hypothesis that are inferable.

we need to replace the deterministic AND with a different way of combining evidence.


The true label of an example is y(x) = 1 if the example is positive and y(x) = −1 if the example is negative.

x ∈ P ⇒ s(x) = y(x) = 1 and x ∈ U ⇒ s(x) = −1 i.e., the true label y(x) of an unlabeled example is unknown.


PTK allows for breaking the production rules.

pTK generalizes STK generating an extremely rich feature space, which results in higher generalization ability.


In addition, the complexity of these algorithms is very high.

we propose a different and simple model integrating FrameNet parsing information in this paper.


Whereas, by intuition, many high-frequency functional words would not be helpful to the estimation of the similarity given in Eq.(2).

in the preprocessing stage, we compute the word frequencies per dataset, and then remove the high frequency words (top 1% in frequency list) in each segment.


For example, the words walking, afoot, ambulate constitute a single synset which has gloss representations as follows, walking: the act of traveling by foot afoot: traveling by foot ambulate: walk about To lift the limitations of dictionary glosses which are fairly short with insufficient vocabulary, we utilize the glosses of related senses since we assume that words co-occur in one sentence share related senses and the more glosses two senses share, the more similar they are.

we extract not only glosses of target synset, but also the glosses of the hypernym, hyponym, meronym, holonym and troponym synsets of the target synset to form a synset context.


We then set the two cells to zero, and obtain an updated matrix as follows, b c a d a 0 0 1 0 b 0 0 0 0 c 0 0 0 0 d 0 0 0 1 In Table 2, we found that cell(0,2) and cell(3,3) have values greater than zero.

a and b will be extracted the common terms.


In this STS-TYPED task, we use string kernel and WordNet to determine whether two terms are similar and increase the number of counts if their similarity exceeds a certain threshold.

we have chosen 0.4, 0.5 and 0.6 in a heuristic manner and obtained three different runs.


However, approaches including word and n-gram features also performed well (Bär et al., 2012; Sarić et al., 2012), and the 2013 STS task focused more on predicting similarity for text pairs from new domains.

for the three variations of our system that we were allowed to submit, we used stacking (Wolpert, 1992) to combine PERP with word and ngram features and applied the domain adaptation approach outlined by Daume III (2007) to facilitate generalization to new domains.


However, we did not map the STS 2013 subtasks to STS 2012 subtasks.

the specific copies of features were only active for OnWN test set examples.


Intuitively, if two sentences share more strings, they are considered to have higher semantic similarity.

we create 12 string based features in consideration of the common sequence shared by two texts.


The hyperparameters (α, β) were set to 1 num of topics .

each item in the test set is represented as a topic distribution.


The Wiktionary dictionary stores the information in a semi-structured way using Wikimedia syntax, where a single page represents a single word or phrase.

we developed a parser to extract relevant information.


Many of these sentence pairs where quite similar in their semantic topic but contained financial expressions or numbers that differed.

these expressions where extracted and compared against each other with a descending score.


Detecting two similar text fragments is a difficult task in cases where the similarity occurs at semantic level, independently of the implied lexicon (e.g in cases of dense paraphrasing).

similarity estimation models must involve features other than surface aspects.


Our analysis of the official results showed the main issue was normalization.

we performed a manifold of new experiments, using the same configuration as in run AE, but applying other normalization strategies: (a) z-score normalization, but ignoring the FNWN dataset (given its shift through low values); (b) z-score normalization, but considering independent means and standard deviations for each test dataset; and (c) without normalizing any of dataset (including the regressor one).


Second, in many applications, an effective means of incorporating distributional semantics is Random Indexing (RI).

we consider three different representations possible within Random Indexing (Kanerva et al., 2000;Sahlgren, 2005).


These metrics give similarities between two strings, rather than two sets of strings as we have with NE 1 and NE 2 .

we follow previous work in greedily aligning these named entities (Lavie and Denkowski, 2009;Šarić et al., 2012) into pairs.


For example, while run 4 is better overall, runs 1-3 outperform it on all but the headlines and FNWN datasets, i.e., the test datasets that were not present in the training data.

it is clear that the source-specific models are beneficial when the training data is in-domain, but a combined model is more beneficial when no such training data is available.


We evaluated our models based on a leave-one-out cross-validation across the 3 training datasets.

for each of the training datasets, we trained a separate model using features from the other two.


The most popular approach to evaluate the semantic similarity of words and texts consists in using the semantic knowledge expressed in ontologies (Resnik, 1999); commonly, WorldNet is used for this purpose (Fellbaum, 2005).

unfortunately, despite the great effort that has been the creation of WordNet, it is still far to cover all existing words and senses (Curran, 2003).the semantic similarity methods that use this resource tend to reduce their applicability to a restricted domain and to a specific language.


Since linguistic processing can reveal deeper similarity relationships, we also look at the translation task at different granularities of information: plain text (R for regular) , after lemmatization (L), after part-of-speech (POS) tagging (P), and after removing 128 English stop-words (S) 1 .

we obtain 4 different perspectives on the binary relationship between S 1 and S 2 .


Our second hypothesis is that the more languages we use as the basis for determining translation similarity between the MWE and its component words, the more accurately we will be able to estimate compositionality.

rather than using just one translation language, we experiment with as many languages as possible.


However, we cannot locate any of the component translations in the translation of kick the bucket.

we conclude that it is non-compositional.


It is difficult to determine where concepts of happiness such as quality of relationships or degree of achievement in life fit in with subjectivity.

we do not claim to be measuring subjectivity and instead we use the general term of 'psychological state', referring to "the way something [a person] is with respect to its main attributes" (Miller, 1993).


We apply a number of restrictions to the resulting pairs of implicit arguments and antecedents to minimize the impact of errors from preprocessing: -The aligned PAS should consist of a different number of arguments (to minimize the impact of argument labeling errors) -The antecedent should not be a resolved pronoun (to avoid errors resulting from incorrect pronoun resolution) -The antecedent should not be in the same sentence as the implicit argument (to circumvent cases, in which an implicit argument is actually explicit but has not been recognized by the parser) We apply the identification and linking approach to the full R&F data set of aligned predicates.

we induce a total of 701 implicit argument and antecedent pairs, each in a separate document, involving 535 different predicates.


Twitter and Facebook were credited for providing momentum for the 2011 Arab Spring and Occupy Wall street movements (Ray, 2011), (Skinner, 2011).

efforts to model social conversations would provide valuable insights into how people influence each other through emotional expressions.


Also research in emotion analysis of text suggest that 1 https://dev.twitter.com/docs/using-search Table 2: Average distribution of emotions across  the folds  Emotion  Training  Test  Anger  58410  6496  Fear  13692  1548  Joy  74108  8235  Sadness  63711  7069  Surprise  2533  282  Love  31127  3464  Total  243855  27095 lexicon based features were effective compared to that of n-gram features in an emotion classification of text (Aman and Szpakowicz, 2008;Mohammad, 2012a).

we decided to use the lexicon to derive features for text representation.


Unlike the automatic lexicons, the general purpose lexicons do not offer numerical scores.

we looked for presence/absence of words in the lexicons to obtain the feature vectors.


It is thus likely that existing evaluation datasets give a wrong picture of the true performance of similarity measures.

in this paper we investigate whether similarity should be measured on the sense level.


This is one of the earlier described normalization approaches.

we argue that the Wikipedia Link Measure is a special case of our more general ESA on senses approach.


This iterative process continues until we reach the completed puzzle in grid (c).

in WSD terminology, with each cell we disambiguate, a new grid is constructed, in which knowledge is passed on to each consecutive iteration.


For this experiment no means of optimisation were applied.

personalised pageRank was not used, and traditional pageRank took on a uniform random surfing vector.


Yet if φ is a centrality measure of In-Degree or Betweenness Centrality, all three senses of cup now have the same score, zero.

in our results the first sense is chosen which is incorrect.


Secondly with each disambiguation at lower levels of polysemy, a more stable context is constructed to perform the disambiguation of much more polysemous lemmas later.

in Figure 5 an iteratively constructed subgraph with cup already disambiguated, would mean the other two senses of cup would no longer be present.


It suggests that the word relatedness problem can be reduced to short text relatedness by adding some related context with the given word.

to improve the ESA performance for word relatedness, we propose an approach for context enrichment of words.


However, WordNet may contain more than one synset for a word, where each synset represents a different semantic sense.

we obtain more than one contexts for a given word, by concatenating the different synsets.


The HAL model employs a sliding context window to compute a word-word cooccurrence matrix, which we will note A, in which value a ij is based on the number of times context word w j appears within the context window of target word w i .

words that share cooccurrents will be closer in word space.


As we have seen, there is only a small set of different You Will challenges that are reciprocal actions for a diverse set of I Will challenges.

many I Will challenges will end up having the same You Will challenge.


In Polish sentences involving the verb LECZYĆ 'treat, cure', the object of treatment was variously marked as Agent, Beneficiary, Patient or Source when using VerbNet roles, and as Agent, Beneficiary, Experiencer, Patient, Recipient or Result when using Sowa's system.

in Zwierzę jest leczone z tych chorób 'An animal is treated for these diseases', in the VerbNet experiment the animal was marked as Beneficiary (by 3 annotators), as Patient (×3) and as Source (×1), and in the Sowa experiment -as Beneficiary (×2), as Patient (×2), as Recipient (×2) and as Result (×1).


This code performs a transformation over the object at every step, generating a new state in a dynamic model of the event denoted by the a given predicate.

the event 6 is decomposed into frame-by-frame transformations representing the ν-transition from Section 3.2.


If these values are left unspecified, the software either fails to compile or throws an exception.

we are forced to arbitrarily choose a direction of motion (as well as direction of rotation, speed of rotation, speed of motion, etc.).


Studies have shown that people of different cultures can perceive emotions very differently, and having forced choice options cannot always capture their true perception (Gendron et al., 2014).

we did not restrict the response of the workers in any way.


The performance of a segmenter cannot simply be measured by false positive and false negative boundaries compared to the true boundaries because, if the predicted boundary is only one sentence away from the true boundary this could still be very close, e.g., if the next true topic boundary is 30 sentences away.

the relative proximity to true boundaries should also be considered.


For every true boundary in a document, RS predicts a boundary drawn from a normally distributed set around the true boundary with scalable standard deviation σ.

smaller values for σ result in better segmentations because the probability of selecting the true boundary increases, e.g., for σ = 2, more than 68% of all predicted boundaries are at most 2 sentences away from the true boundary and more than 99% of all predicted boundaries are located within a range of 6 sentences from it.


To take a common example: If Sue's height is 180cm, she may be appropriately described as a tall woman, but probably not as a tall basketball player.

what counts as tall can vary from context to context, with the most relevant contextual parameter being a comparison class relative to which the adjective is interpreted (e.g., the set of women, the set of basketball players, etc.).


(In Section 5 we will discuss how such a threshold may be computed.)

the meaning of 'tall' involves a classifier for tallness, κ tall , of the following type: We define this classifier as a one-input perceptron that compares the perceived height h of an individual x to the relevant threshold θ determined by a comparison class c. if θ : type → R + and r : t ctxt , then: Simplifying somewhat, we can represent the meaning of 'tall', tall, as a record specifying the type of context (t ctxt ) where an utterance of 'tall' can be made, the parameter of the tallness classifier (the threshold θ), and a function f which is applied to the context to produce the content of 'tall'.


The context of utterance in the sit field will include the height-sensor reading, which means that the sensor reading is part of the proof of the sit-type indicating that x is tall (or not, as the case may be).

to decide whether to refer to some individual x as tall or to evaluate someone else's utterance describing x as tall, an agent applies the function tall.f to the current situation, represented as a record r : T ctxt .


In our characterisation of the context-sensitivity of vague gradable adjectives in Section 4.1, we have focused on their dependence on general comparison classes corresponding to types of entities (such as Human, Woman, etc) with expected properties such as height.

in contrast to DeVault and Stone (2004), who focus on the local context of discourse, we have focused on what could be called the global context (an agent's experience regarding types of entities and their expected properties).


Context provides fundamental cues to disambiguating polysemous words, and noun modifiers typically act as important disambiguating contexts for the nouns.

we think that it is more productive for computational systems to handle modifier-triggered disambiguation as a special case of the more general class of modification effects, than to engage in the quixotic pursuit to determine, a priori, what's the boundary between a word-sense and a "pure" modification effect.


8 While the task of deciding if u is in class v is typically framed (also by distributional semanticists) in binary, yes-or-no terms, all proposed measures return a continuous numerical score.

9 we conjecture that they might be wellsuited to capture the graded notions of class membership and typicality we recorded in NBP.


Recall that mult returns the component-wise product of the vectors it combines.

modification under mult is carried out by picking only those features of the head that are also present in the modifier, and enhancing them by a factor given by the modifier's feature value.


This is due to the fact that the features of a phrase composed using mult are a subset of the features of the head, 16 and in this case the head is the same as the category.

by definition, weedsprec yields a score of 1 for every pair, the variance is null and hence the correlation is unde-  fined.


Using the 300-D GloVe word embeddings was very helpful: LSTM-RNN performed on par with DRNN in the fine-grained task, and with CNN in the binary task.

taking into account both tasks, LSTM-RNN with the 300-D GloVe word embeddings outperformed all other models.


More generally, if the optimal subbases of K are small (say twice smaller that K), it can be rightfully argued that dropping so many statements for the sake of linguistic evidence is not a viable debugging strategy.

a more plausible application scenario is one in which the search space has been previously circumscribed, either by setting a maximal (small) number of statements to discard, or by identifying a set of potentially erroneous statements, through axiom pinpointing, as explained in section 6.


Unfortunately, if Ψ + Γ is the set of all consequences of Γ which can be built this way, there is in general no finite subset Ψ Γ of Ψ + Γ such that Ψ Γ |= ψ for all ψ ∈ Ψ + Γ .

the complex classes to be used must be selected, which is not trivial.


It is also hard to do consistently, and can only be crowdsourced in limited settings (Feizabadi and Padó, 2014).

even though multiple systems for implicit SRL exist (among others, Tonelli and Delmonte (2011), Laparra and Rigau (2012), Silberer and Frank (2012)), results are still relatively poor.


In this paper, we focus on the fact that the corpora that exist for implicit SRL differ not only in the semantic role frameworks used (FrameNet vs. PropBank), but also in genre (newswire vs. novels), and classes of annotated predicates (verbs vs. nouns).

they are generally regarded as incompatible, and previous work has concentrated on getting most out of individual corpora, or spending annotation effort on focused extensions of these corpora.


The overall pattern of a major boost to recall and a minor one to precision are also stable across roles.

corpus combination seems to benefit all roles as well.


The semantics parallel one another: both sentences have a causal argument responsible for the event, an argument in motion, and a path that specifies the initial, middle, or final location, state or condition of the argument in motion (Hwang et al., 2013).

if the semantic interpretation is strictly based on the expected semantics of the verb and its arguments, it fails to include the relevant information from the CMC.


After a series of experiments, it was determined that the part of speech and the dependency relation features might be too fine grained to provide useful information to the classifier.

all of the features expressed by the part of speech and the dependency relation are featurized in the following manner.


Additionally, some of the existing syntactic differences are neutralized by the cause and undergoer argument pre-processing stage described in Section 3.5.2.

it stands to reason that most of the useful contributions come from the lexical items themselves and the semantics of the verb and its arguments.


Both the BioNLP-EE and *SEM corpora capture negations within-and therefore aligned with-syntactic analyses.

they deploy annotation schemes which assume downstream consumers of some granular negation representation, learnable from the annotated resource(s).


predicate nodes corresponding to the deep syntactic subject: observe how activator is 'subj' both to do and activate.

whether to include a subject into a verb scope (e.g.


Hence, the span of a Negated-Predicate may include text spans that were originally not part of the corresponding BioScopeScopeSpan annotation.

we built a statistically trained system to predict whether the span of a descendant node of a NegatedPredicate should, or should not, be included in reverse mapping that NegatedPredicate to the corresponding BioScopeScopeSpan.


We do not make any effort to evaluate the truth value of the propositions, only the expressed level of belief in them held by the writer.

a strongly held false belief would not appear any different from a strongly held true belief.


As already explained (Section 2), we take the terms "belief" and "factuality" to refer to the same phenomenon underlyingly (with perhaps different emphases).

the FactBank annotation is basically compatible with ours.


In this paper, we present Non-Orthogonal ESA (NESA) which represents more fine grained semantics of a word as a vector of explicit concept dimensions, where every such concept dimension further constitutes a semantic vector built in another vector space.

nESA considers the concept correlations in computing the relatedness between two words.


However, such lexical resources require domain expertise for defining the hierarchical structure, which makes their creation very expensive.

distributional semantic models (DSMs) have achieved much attention as they utilize available document collections like Wikipedia, and do not depend upon human expertise (Harris, 1954).


Table 1 shows that the top 5 Wikipedia concepts retrieved for "football" and "soccer" do not share any concept, however, the concepts may exhibit relatedness to each other.

eSA model assumes that words can be related only if they co-occur in the same articles.


Previous studies (Gabrilovich and Markovitch, 2007;Cimiano et al., 2009;Hassan and Mihalcea, 2011) show that ESA performs better than LSA for computing text relatedness.

it is important to consider the relatedness between dimensions in the ESA model, rather than considering them orthogonal, and also without losing the explicit property of ESA model at the same time.


In addition to represent the words as distributional vectors, where each dimension is associated with a Wikipedia concept as in ESA model, NESA also utilizes a square matrix C n,n (n is the total number of dimensions) containing the correlation weights between the dimensions.

to obtain the relatedness score between the words w1 and w2, NESA formulates the measure as follows: (1) where w1 n,1 and w2 n,1 are the corresponding distributional vectors consisting of n dimensions.


This transformation allows the computation of the correlation weights between the concept dimensions.

a transformation matrix E m,n can be built, where each column corresponds to a transformation vector for each concept dimension.


ESA represents the semantics of a word with a high dimensional vector over the Wikipedia concepts.

each concept dimension is transformed into a column vector of mx1, where m is the total number of Wikipedia concepts.


It uses the distributional information of such hyperlinks only over the whole Wikipedia corpus for representing a concept by a high dimensional distributional vector.


VSM-Text does not capture the semantics of Wikipedia concepts as the textual description in Wikipedia article also contains generic terms which are not enough to specify the precisely semantics of Wikipedia concepts.

vSM-Hyperlink achieved noticeable improvement over vSM-Text as vSM-Hyperlink builds the semantic profile by using hyperlinks in the article content.


and keyphrase extraction is central to all of them.

it could be said that keyphrase extraction is a fundamental NLP task, improvements in which could cascade into improvements in higher-level applications that build upon it.


To make sure terms with higher statistical weights are visited more often we would want higher transition probabilities between them but lower transition probabilities between terms with lower weights.

we use the product of the term pair's weights as a factor into the weight of the edge between them in the graph.


This is the motivation behind KP-Miner's elimination of candidates that do not occur on their own.

to consider all candidates, while countering the keyphrase fragments problem, we calculate the subsumption count over a much larger portion of the ranked terms compared to KP-Miner.


Another way of looking at the presented algorithm is as a term weighting scheme.

an interesting future direction would be to investigate whether replacing traditional term weighting schemes, e.g.


Discussion We mentioned above that toolkits like GIZA++ cannot perfectly estimate translation relationships between words in different languages.

we have to face situations of 'noisily' weighted links between words in the same network version of reference language R. Typically, a higher chance of mismatch occurs in the case of bigrams.


Since it is likely that, e.g., P fr [HUMAN|être] and P fr [BEING|être] will also be positive, we can expect weighted links in the French network version of English between HUMAN and LOVE as well as between BEING and LOVE.

besides 'true' semantic relations, our approach also captures, though unintentionally, co-occurrence relations.


the basic terms of the Schank and NSM systems) must be included in the defining set, and to these we must add at least one vertex from every directed cycle.

the problem of finding a defining set is equivalent to finding a feedback vertex set, (FVS) a problem already proven NP-complete in Karp (1972).


This way, the lexical entries can be kept highly abstract, and for the most part, universal.

the verb go will have a source and a goal.


While resources such as WordNet (Miller, 1995) contain many general relations and subsequently have seen widespread adoption, developing this type of rich resource for new languages or for new domains is prohibitively costly and time-consuming.

automated approaches are needed and, in order to create such a lexical-semantic database, a first step is to develop accurate techniques for classifying the type of lexical-semantic relationship between two words.


Its main advantage is that all the dependency relations of a target word, extracted from different sentences, are incident edges to its corresponding node in the graph.

words that never co-occur in the same context in corpus, are linked in the graph through bridging words: words that appear in a dependency relation with each member of the pair but in different sentences.


Word pairs with similar offsets can be interpreted as word pairs with the same semantic relation.

given a target word pair (x, y), the vectorial representation is calculated from the difference between its vectors, i.e., v((x, y) Note that this operation is dependent on the order of the arguments and is therefore potentially able to capture asymmetric relationships.


Corpora Many pattern-based systems increase the size of the input corpus in an attempt to overcome data sparsity and to achieve a better recall.

in our experiments, we train our systems using two corpora of different sizes: the British National Corpus (BNC), a 100 million-word corpus, and a Wikipedia dump created from 5 million pages and containing 1.5 billion words.


Only 109 of the 1453 senses identified in Portuguese did not have an equivalent verb sense in English identified in Propbank.

as the frame files of such 109 verbs could not obtain the fields brought from Propbank automatically, they required manual edition.


However, we deem some of the related algorithms to have good potential applicability for this task.

we included two strong algorithms from the closest related work as baseline solutions to the problem.


The SVMs trained with bigrams consistently produced the best results for the 4-fold cross validation.

our testing phase used the SVMs trained with the bigrams.


Text layout supports a large part of semantics and participates to the coherence of the text; it thus contributes to the elaboration of the discourse.

we adapted the discourse analysis to treat the layout, according to the following principles: -a DU corresponds to a visual unit (a bloc); -two units sharing the same role (title, paragraph, etc.)


However, more traditional mention-pair approaches still have some important advantages.

a mention-pair model is easy to implement and allows for fast prototyping.


All these rule-based decisions can be integrated into EFMP in a straightforward way.

the uninformative pairs are simply excluded from the further processing.


It is straightforward to see that J T (I) is an antimonotone function.

standard frequent item mining algorithms can be easily adapted to cover the Jaccard index.


We have made several adjustments, starting from the configuration, suggested in the BART distribution for the OntoNotes/CoNLL data.

we have modified the mention detection module, improving the treatment of coordinations and eliminating numeric named entities (PERCENT, MONEY etc).


Reliable automatic aspectual classifications are expected to enhance existing solutions to temporal relation classification.

if our LVF-model improves such a solution, we can conclude that our learned aspectual values are reliable.


For other languages, this kind of information, when it is available, is generally not contained in a single lexicon.

a further interesting research direction would be to evaluate the applicability of our technique to suitable information from distributed resources.


That is, on a scale from most literal to most metaphorical, instances may have different degrees of metaphoricity (or literalness).

we also evaluate pairs where the individual instances have not been explicitly labeled as metaphorical or literal; instead, they have been marked according to whether one instance is more metaphorical than the other.


for each of the Hypothesis 1 Pairs we asked annotators to mark which instance is more emotional, as shown in Questionnaire 4 below: Questionnaire 4: Which of the two given sentences conveys more emotion?


This difference is significant with greater than 95% confidence, using the binomial exact test.

results from both experiments support Hypothesis 1.


This dataset included all possible pairs of instances associated with each verb in the Master Set.

in addition to pairs where one is highly metaphorical and one highly literal, this set also includes pairs where both instances may be highly metaphorical or both highly literal.


It follows from Hypothesis 1 that terms with the same degree of metaphor will be more substitutable than terms with different degrees of metaphor.

nLG systems can benefit from taking the degree of metaphor into account.


In case of the standard firstorder intersective analysis, the premises of Fr-211 would be translated as: which is a contradiction given that small and large are contradictory predicates.

due to the principle of explosion everything, including the conclusion and its negation, would be entailed from the premises.


we get the inference encoded in (∅FUN↓).


in Table 5, where criticize-1 is an instance of AF since the verb "to criticize" bears that signature: whatever affirmative "to criticize" embeds, it is factual 2 .

all subclasses of Embedded are primitive concepts (given by the verb signatures).


In contrast, word sense disambiguation (WSD) assumes there exists an already-known sense inventory, and the sense of a word type is disambiguated according to the sense inventory.

clustering methods are generally applied in WSI tasks, while classification methods are utilized in WSD tasks.


Concretely, deciding the number of senses is the same problem as the clustering problem in WSI task, and sense embedding is a potential knowledge representation for WSI task.

sense embedding methods are naturally applicable to WSI.


As WE-Kmeans and SE-WSI-fix learn sense centroids in the same vectors space, while the latter performs joint learning.

the joint learning is better than learning separately.


Since we would like to test the effectiveness of adding supervision, we treat all verbs in the evaluation set as unsupervised, with no initialization of θ.

to compare apples-to-apples, we calculate the nPU, niPU, and F1 of the Kawahara et al.


We show that, contrary to expectations, the deletion of non-subsective adjectives from a sentence does not necessarily result in non-entailment.

in applications such as information extraction, it is often possible to extract true facts about the N from sentences involving a non-subsective AN.


In our notation: Here p(R m i |Q m j ) refers to the probability that a response R by author i contains marker m given that the quoted post by j also contains m. How much coordination C there is in i's responses to j corresponds to the difference between this conditional probability and the prior probability p(R m i ) for author i, i.e., the probability that any response by i contains a linguistic marker of category m. Given the sparsity of data in online discussion fora with regards to repeated interactions between the same individuals i and j, we compute a score for each Q-R pair (rather than for the set of Q-R pairs between specific authors i and j).

the conditional probability in Equation [1] corresponds to a variable that takes value 1 if both Q and R contain m and 0 if only Q does (and is undefined if Q does not contain m).


Contrary to our first hypothesis, we found more accommodation in disagreeing responses than in agreeing ones.

if speakers fully align on content, there seems to be less need to also align on style; in contrast, when there is a content disagreement, speakers may want to maintain rapport by exhibiting style accommodation.


4 Both LDA-based models (Misra et al., 2009;Riedl and Biemann, 2012) and GRAPHSEG rely on corpus-derived word representations.

we evaluated on the Manifesto dataset both the domainadapted and domain-unadapted variants of these methods.


The synsets are filtered, keeping only those where the part-of-speech tag of the synset matches the part-of-speech tag of the word.

the semantic ambiguity of words across different grammatical categories (such as the noun kom 'bowl' and the verb kom 'come') has never formed an obstacle.


Traditionally, the latter is annotated by experts through a rather costly procedure.

sometimes, only noisy annotations obtained via automatic labeling mechanisms are available.


The results demonstrated that the empirical and semantic properties of the compounds and the head nouns play a significant role.

psycholinguistic experiments as well as computational models are advised to carefully balance their selections of compound targets according to compound and constituent properties.


3 Further, removal of query hash- 3 A tweet that has a seemingly favorable hashtag may in tags may result in a tweet that no longer expresses the same stance as with the query hashtag.

we manually annotate the tweet-target pairs after the pre-processing described above.


In Table 1 we report similar linguistic nature of suggestions across these datasets, which presses for domain independent approaches.

as a deviation from previous studies, this work investigates the generalisation of the problem of suggestion detection i.e.


Targeted suggestions were mainly the ones which suggest improvements in a commercial entity.

online reviews remains the main focus, however, there are a limited number of works focussing on other domains too.


These embeddings determine 7 Best predict vectors on http://clic.cimec.unitn.it/composes/semantic-vectors.html 8 http://nlp.stanford.edu/projects/glove/ 9 Dependency-Based on https://levyomer.wordpress.com/2014/04/25/dependencybased-word-embeddings/ the context of a word on the basis of linguistic dependencies, instead of window based context used by COMPOSES.

deps tends to perform better in determining the functional similarity between words, as compared to COM-POSES.


This tends to decrease the precision and increase the recall, but results in an overall decrease of F-1 score in most of the runs.

we do not report the results of these experiments.


Preprocessing tends to decrease the performance in all the experiments.

none of the experiments reported by us use pre-processing on tweets.


Since suggestions tend to exhibit similar linguistic nature, irrespective of topics and intended receiver of the suggestions, there is a scope of learning domain independent models for this task.

we apply the discussed approaches both in a domain dependent, and domain independent setting, in order to evaluate the domain independence of the proposed models.


5 Moreover, since these methods use asymptotic Gaussian or Cauchy random projection matrices R with E(R) = 0, their resulting vectors cannot be adjusted and transformed using weighting techniques such as PPMI.

these methods often do not outperform neural embeddings and combinations of PPMI weighting of count-based models followed by matrix factorization-such as the truncation of weighted vectors using singular value decomposition (SVD).


These results, however, are achieved without the need for heavy computations.

instead of hours, models can be built in a matter of a few seconds or minutes.


As mentioned earlier, this is due to the fact that a random projection matrix in RI has an asymptotic standard Gaussian distribution (i.e., transformation matrix R has E(R) = 0).

ppMI weights for the RIinduced vector elements are undefined.


The Yago project (Suchanek et al., 2007) shares unary properties associating hundreds of thousands of descriptive categories manually created by the Wikipedia community to DBpedia entities (Auer et al., 2007).

a mechanism to recognise paraphrases can make a shortcut between a natural language expression and a set of entities.


in an answer such as 'Mozart was born in Salzburg', we can disregard the words 'Mozart', 'was' and 'born' on account of their previous mention, leaving only the relevant information 'in Salzburg'.


But under the current setting of our distributional similarity approach, the noun Aufsichtsrat (supervisory board) that is semantically related to the noun Vorstand (advisory board) is also marked as GIVEN and thus excluded from the content assessment.

all material in the answers is excluded from the alignment and the CoMiC system fails to classify the student answer as a correct answer.


Furthermore, φ(t) and ψ(s) are binary vectors indicating whether a KB symbol/word is present or absent in t/s.

f (t) and g(s) are the embeddings of t and s and S t/s scores their similarity by taking their dot product.


They also show that our approach can produce both L-and E-related lexicalisations.

for instance, for the property PROGRAMMINGLANGUAGE, our model produces the lexicalisation "programming language for" which is clearly an L-lexicalisation that can be directly derived from the property name.


According to Montague, proper names (arguments of type e) denote individuals, while verbs and common nouns (predicates of type e, t ) denote sets of individuals.

'Amsterdam' denotes an individual, while 'dog' denotes the set of dogs.